Indoor Positioning Systems (IPS) traditionally rely on odometry and building infrastructures like WiFi, often supplemented by building floor plans for increased accuracy. However, the limitation of floor plans in terms of availability and timeliness of updates challenges their wide applicability. In contrast, the proliferation of smartphones and WiFi-enabled robots has made crowdsourced radio maps – databases pairing locations with their corresponding Received Signal Strengths (RSS) – increasingly accessible. These radio maps not only provide WiFi fingerprint-location pairs but encode movement regularities akin to the constraints imposed by floor plans. This work investigates the possibility of leveraging these radio maps as a substitute for floor plans in multimodal IPS. We introduce a new framework to address the challenges of radio map inaccuracies and sparse coverage. Our proposed system integrates an uncertainty-aware neural network model for WiFi localization and a bespoken Bayesian fusion technique for optimal fusion. Extensive evaluations on multiple real-world sites indicate a significant performance enhancement, with results showing ∼ 25% improvement over the best baseline.

Traditional Indoor Positioning Systems (IPS) use odometry, WiFi, and often building floor plans for accuracy. However, floor plan limitations have shifted attention to crowd-sourced radio maps, popularized by smartphones and WiFi-integrated robots. These maps pair locations with Received Signal Strengths (RSS) and reflect movement patterns similar to floor plans. Our research explores using radio maps as an alternative to floor plans in IPS. We've developed a new framework that combines an uncertainty-aware neural network for WiFi positioning with a Bayesian fusion method. Testing in real-world scenarios showed about a 25% performance increase compared to the leading baseline.